Skip to main content

SimCSE: Simple Contrastive Learning of Sentence Embeddings

Author(s): Gao, T; Yao, X; Chen, Danqi

To refer to this page use:
Abstract: This paper presents SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself in a contrastive objective, with only standard dropout used as noise. This simple method works surprisingly well, performing on par with previous supervised counterparts. We find that dropout acts as minimal data augmentation and removing it leads to a representation collapse. Then, we propose a supervised approach, which incorporates annotated pairs from natural language inference datasets into our contrastive learning framework, by using “entailment” pairs as positives and “contradiction” pairs as hard negatives. We evaluate SimCSE on standard semantic textual similarity (STS) tasks, and our unsupervised and supervised models using BERTbase achieve an average of 76.3% and 81.6% Spearman's correlation respectively, a 4.2% and 2.2% improvement compared to previous best results. We also show-both theoretically and empirically-that contrastive learning objective regularizes pre-trained embeddings' anisotropic space to be more uniform, and it better aligns positive pairs when supervised signals are available.
Publication Date: 1-Jan-2021
Citation: Gao, T, Yao, X, Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings, 6894 - 6910
Pages: 6894 - 6910
Type of Material: Journal Article
Journal/Proceeding Title: EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.